Core Insights - The article emphasizes the importance of data quality governance for industrial enterprises in the context of the digital economy and new industrialization [1] - It highlights the challenges faced by traditional industrial companies in effectively transforming vast amounts of data into actionable insights due to issues like "data silos" and "data inaccuracy" [1] Group 1: Data Governance Concepts - The white paper clarifies key concepts related to data governance, such as master data, static data, source governance, and end governance, providing a solid theoretical foundation for practical guidance [2] - This clarification helps enterprises to plan governance strategies from a holistic perspective rather than a fragmented one [2] Group 2: Data Governance Maturity Model - The white paper introduces a five-stage maturity model for data quality governance in industrial enterprises, derived from extensive research on domestic and international practices [3] - This model outlines a progression from basic standards to intelligent governance, enabling companies to accurately identify their current stage and set clear goals for advancement [3] Group 3: Stages of Data Governance - **Stage 1: Coding Management (Initiation Stage)** - Focuses on establishing unified coding rules to resolve data identification issues, emphasizing the importance of foundational governance [4] - **Stage 2: Master Data Management (Transition Stage)** - Expands governance to standardizing shared data, ensuring consistency and accuracy of core master data across the enterprise [5] - **Stage 3: Static Data Governance (Breakthrough Stage)** - Involves comprehensive governance of all static data, enhancing quality control through business logic validation and algorithmic checks [6] - **Stage 4: Source and End Collaboration Governance (Mature Stage)** - Represents a mature phase where governance covers the entire data lifecycle, ensuring data is reliable and usable in decision-making [7] - **Stage 5: Intelligent All-Domain Governance (Intelligent Stage)** - Aims to govern unstructured data using advanced technologies like AI and NLP, significantly improving governance efficiency [9] Group 4: Value and Outlook - The release of the white paper provides significant industry value by offering a complete action guide for industrial enterprises struggling with data issues, helping them save time and costs [10] - It promotes standardized concepts and frameworks to enhance communication and collaboration across different departments and stakeholders [10] - The white paper serves as a valuable resource for Chief Data Officers, IT leaders, and decision-makers, aiding in the strategic transformation of data governance into a value-creating asset [10]